Face morphing attacks pose a severe security threat to face recognition systems, enabling the morphed face image to be verified against multiple identities. To detect such manipulated images, the development of new face morphing methods becomes essential to increase the diversity of training datasets used for face morph detection. In this study, we present a representation-level face morphing approach, namely LADIMO, that performs morphing on two face recognition embeddings. Specifically, we train a Latent Diffusion Model to invert a biometric template - thus reconstructing the face image from an FRS latent representation. Our subsequent vulnerability analysis demonstrates the high morph attack potential in comparison to MIPGAN-II, an established GAN-based face morphing approach. Finally, we exploit the stochastic LADMIO model design in combination with our identity conditioning mechanism to create unlimited morphing attacks from a single face morph image pair. We show that each face morph variant has an individual attack success rate, enabling us to maximize the morph attack potential by applying a simple re-sampling strategy. We will publish our code and pre-trained models upon the acceptance of this paper.

Biometric Sample Quality Assessment (BSQA) estimates the usefulness of the captured image based on its utility for the recognition task. In this regard, the majority of studies have been proposed in the last decade for computing a sample quality score from facial images. In particular, methods that learn a regressor from pseudo-labels have obtained reliable results on various benchmarks. However, they fail to correctly estimate the quality of samples having both, low quality and low intra-class variability. This paper proposes a new BSQA approach, Perceptual Classifiability Ratio for Hand Image Quality Assessment (PCR-HIQA), which computes hand image quality by combining the relative classifiability of the sample with its fidelity-related properties. On the one hand, the classifiability ratio is calculated by mapping the feature representation of the training samples in the angular space with respect to its class centroid to the nearest negative class centroid. On the other hand, the fidelity properties encode the human perception of the input sample quality. Experimental results on the challenging HaGRID database, containing different hand gestures, underline the superiority of the proposed BSQA method which outperforms state-of-the-art techniques by up to 30%.1

Deep neural networks have become prevalent in human analysis, boosting the performance of applications, such as biometric recognition, action recognition, as well as person re-identification. However, the performance of such networks scales with the available training data. In human analysis, the demand for large-scale datasets poses a severe challenge, as data collection is tedious, time-expensive, costly and must comply with data protection laws. Current research investigates the generation of synthetic data as an efficient and privacy-ensuring alternative to collecting real data in the field. This survey introduces the basic definitions and methodologies, essential when generating and employing synthetic data for human analysis. We summarise current state-of-the-art methods and the main benefits of using synthetic data. We also provide an overview of publicly available synthetic datasets and generation models. Finally, we discuss limitations, as well as open research problems in this field. This survey is intended for researchers and practitioners in the field of human analysis.

Biometric data stored in automated recognition systems are at risk of attacks. This is particularly true for large-scale biometric identification systems, where the reference database is often accessed remotely. A popular approach for the protection of the stored templates is homomorphic encryption, which grants privacy protection while maintaining the biometric performance of the unprotected system. However, it introduces a significant computational overhead that can render identification transactions infeasible. To reduce this workload, biometric indexing in the encrypted domain has become a recent research interest. In this work, we show that in such schemes, auxiliary indexing data can leak additional privacy-sensitive information that violate standardized requirements for biometric template protection. In response to this leakage, we propose a novel framework HEBI that protects biometric indexing approaches at a post-quantum security level while requiring a computational effort of only 0.12 milliseconds per cluster.

Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management. Therefore, it becomes crucial to quantify the quality of facial images to ensure that lowquality images are not affecting recognition accuracy. In this context, the current draft of ISO/IEC 29794-5 introduces the concept of component quality to estimate how single factors of variation affect recognition outcomes. In this study, we propose a quality measure (NeutrEx) based on the accumulated distances of a 3D face reconstruction to a neutral expression anchor. Our evaluations demonstrate the superiority of our proposed method compared to baseline approaches obtained by training Support Vector Machines on face embeddings extracted from a pre-trained Convolutional Neural Network for facial expression classification. Furthermore, we highlight the explainable nature of our NeutrEx measures by computing per-vertex distances to unveil the most impactful face regions and allow operators to give actionable feedback to subjects1.

Today, face recognition systems (FRS) are widely used in applications such as forensic and border control systems. Despite the increasing ability of deep neural networks to identify individuals based on their facial images, it remains challenging to recognize faces with long age gaps between reference and probe samples. To improve the robustness of FRS towards aging, training datasets can be enriched with synthetic data by simulating recurring aging effects. Typical aging signs include craniofacial changes during the child-to-adult age transition or textural changes that occur during adult-to-adult aging (e.g., wrinkles or furrows). Building upon the recent achievements of 3D-aware generative adversarial networks, we propose a geometry-aware face age modification algorithm (Age-EG3D) that enables lifespan face age simulation. We demonstrate the effectiveness of our approach by providing a comprehensive performance evaluation and comparison of Age-EG3D to prior works. All code and pre-trained models are available at https://github.com/johndoe133/eg3d-age.